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Shared data plans for families and affinity groups are at the front line of mobile competition.
Last month, Verizon Wireless was the first large operator to announce a shared data plan, but no sooner did Verizon reveals its plans, but its archrival, AT&T, said it would quickly follow suit with a similar program.
The stakes couldn‘t be higher. Mobile operators who can successfully lure families and affinity groups to their shared data plans will achieve new levels of customer lock-in. In turn, that capability will enable them to aggressively cherry-pick a competitor’s base.
Yet as simple as the concept of shared data plans is, they are incredibly hard to revenue assure.
A couple years ago, cVidya, adopted the new tag line, “revenue intelligence” to explain our mission as a company. And frankly, shared data plans are a perfect example of what that moniker means. Service providers can only succeed with shared data plans if they can integrate a wide range of analytic and business control activities.
In this column, I’m going to shine a spotlight on a few crucial functions that operators need to get right here, especially: offering the right price plan, ensuring that any bandwidth throttling is handled correctly, and addressing some new fraud related to shared data plans.
You can look at share data plans in two ways. On the one hand, it’s a fresh, greenfield opportunity that could rake in rich profits. But the flip side is also true: if shared data services are priced wrong and targeted at the wrong subscribers, you could lose a ton of loyal customers in a hurry.
All of which points to the absolute necessity of having a great analytic tool behind you.
Frankly, earlier generations of pricing analytics tools are of limited usefulness in this new area. Normally, when you create new price plans, you analyze the behavior of typical users and determine how the proposed price plan will likely affect current subscribers. But shared data plans introduce a new wrinkle. Tracking individual usage is not enough. You also need to analyze family usage as a whole.
And that factor causes the number of factors and variables to juggle to greatly expand. If you offer the plan to more members of the family, what will encourage more data usage or not? The short and long term effects of these new share data family plans is completely unknown. Will the whole family move? Will the people most attracted to these plans be the family members using iPads or iPhones?
Other vital analytics checkpoints are around comparing the current plan to previous data plans — and also looking at price plans of competitors.
Having a carefully thought out rollout strategy is also key. Which customers should you encourage to move to shared data plans? And how should you treat valuable customers who are already on lucrative individual plans? Clearly, many scenarios need to be tested to ensure that revenue is maximized during the transition — and beyond.
You need a strong tool to simulate the many options. cVidya’s OfferImpact is one tool you should consider. The solution has been recently upgraded with shared data plans in mind.
Everything needs to be tested. But if you’re stuck with a limited homegrown tool, maybe you can‘t investigate enough options. And given the risks involved, you want to look at every viable option.
The more testing, what if analyses, and simulations you can do, the better the chance that your launch and subsequent corrections and revisions will succeed in this brave new pricing world.
Mediating the needs of different family members is a challenge all its own.
Let’s say the kids like to download HD movies. Well, a single High Def (HD) movie consumes about 3 gigabytes of data. And if the family has only budgeted for 10 gigabytes a month, will Mom and Dad be satisfied to reserve one gigabyte for themselves and allocate the remaining 9 gigs to their kids? Probably not. And what happens when the service gets maxed out?
These issues are all brand new challenges, but the current thinking is that Mom and Dad need to control their family’s bandwidth usage and budget of famil members. And that data budgeting might extend to certain types of usage. For instance, parents could allocate a certain percentage to movies vs. email vs. web surfing, etc.
The danger, of course, is that you can make the configuration option so complex that Mom or Dad is elevated to the role of Family CIO.
Though some form of plan management software will clearly emerge, a bigger issue is the operator’s ability to suggest appropriate plans as a family’s understanding of its usage evolves or the family tries to staying in a budget. If you see that most of your data is movies, analytics can suggest, say, a plan that takes advantage of less costly late night downloads.
And analyzing and suggesting the right data share plan is a perfect job for revenue assurance and related analytics solutions. But it must be done proactively. You don‘t want the customer to call and say, “Why is my throughput so slow?”
cVidya’s OfferAdvisor product has the analytics built in to propose the right plans for the families. It can also recommend what the policies should be and at one point you should make offers to specific demographic groups.
This subject of maxing out family usage brings up the issue of network policy control — the rules an operator sets for controlling the bandwidth a family uses each month.
Normally there are two policy options widely used when a data allowance is fully consumed, either: 1) throttle bandwidth down which reduces the quality of the user experience; or 2) charge a fee for an additional data allowance.
A key component in network policy control — and a brand new monitoring point for RA and fraud professionals — is the Policy Control and Rules Function or PCRF system. Now, I’ll admit that as I travel around the world and talk to clients, the first reaction I often get from RA and fraud professionals is “PCRF? What exactly is that?”
PCRF has actually been a mobile network component for a few years now. Basically the PCRF’s job is keep track (meter) of bandwidth consumption, and set policies, like QoS, according to the consumption. The reason you haven‘t heard much about it yet is that the PCRF kind of sat quietly in a corner of the network, and folks who worry about revenue risks never had to be concerned about it — until now.
So why is monitoring PCRF data so important?
Well, it boils down to one simple fact: users can get very upset when you play around with their bandwidth. If you cut a family’s service in error, you’ve got four angry customers to answer to, not just one. You could lose that family’s business forever. And if they’re really upset, they even tell their entire social network about their experience.
This is precisely why you, as an operator, need to be constantly checking the PCRF, ensuring it is operating correctly, and keeping accurate track of bandwidth consumed.
Now, contrary to what you may think, monitoring PCRF data doesn‘t require a big evolution in
RA tools. What it does require, however, is an evolution in an RA or fraud manager’s state of mind.
Is the PCRF data being managed correctly? Are the rules associated with that policy controller working as planned? Did a Fraudster tamper with the policies?
The shift to shared data plans will require constant monitoring of family usage, and in North America, where the vast majority of the mobile business is postpaid, the effect will be to make postpaid billing shops much more on-line and real-time.
Revenue intelligence specialists will definitely need to come up to speed on PCRF. I firmly believe that investigated problems at the policy controller will soon become as important as checking for HLR and CRM glitches are today.
Most RA software vendors are capable of adding the controls. It’s the knowhow that’s the important part, and this PCRF areas is a place where cVidya has relevant experience.
One last area of concern is fraud. A big window we feel fraudsters will try to exploit is the difference between the price offered for one device versus another. Normally Tablets are banned from doing regular Calls and SMS activities, yet certain models permit it (e.g. Samsung Galaxi 7.7 Tab), plus others can be hacked to permit it.
To get a better understanding of this issue, we’ll use prices from Verizon’s pre-service announcement, even though we know that those prices could change when the product launches.
As currently conceived, Verizon’s “Share Everything” service includes a major price gap between Tablets and Smartphones within the shared data plan. Smartphones cost $40 a month and Tablets cost only $10. No doubt, a key factor in that price differential is the fact that Tablets are often used where free WiFi service is available. Smartphones, meanwhile, are more likely used on the town and in moving vehicles where you need to access the relatively more expensive 3G radio network.
Household might be tempted to commit “small” fraudulent activities, e.g., they can take advantage of this price disparity by fooling the operator into thinking an Smartphone is a Tablet. Tampering with the SIM card or other device interface could accomplish this objective.
So it’s important for operators to be aware of these tricks. Using the Verizon Shared Everything Plan as an example, if a family has 3 Smartphones masking as Tablets, the equivalent lost revenue could be $70 per month and over a $800 a year. That’s huge.
One solution is to constantly monitor the type of equipment the SIM card is being used in and base the subscription on the equipment ID, not just a SIM card. Another solution is to verify that every SIM card sold to a tablet is associated with a data plan only in the Operator’s network, and to base the pricing on the SIM capabilities and not the type of device. cVidya has prepared for this issue and incorporated that capability in our FraudView product.
In conclusion, the new shared data family plans represent a big paradigm shift. To win this emerging assurance area, you must first offer the right price plans to the right subscribers -- and be able to quickly adapt to what competitors are offering. Second, you must take steps to stop insider and equipment swapping fraud. Finally you must wrap tight business controls around policy-driven bandwidth throttling that’s bound to anger subscribers if they are not implemented correctly.
So, may the Olympics for shared data plans begin! And if you plan to be a champion in this arena, don‘t forget to compete with the very best revenue intelligence tools and knowhow.
Copyright 2012 Black Swan Telecom Journal